Research and application of data warehouse and data mining technology in medical field

2015 ◽  
Author(s):  
Zhi-Gen Hu ◽  
Jian-Ping Li ◽  
Lei Hu ◽  
Yujun Yang
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Medical Field ◽  
Mining Technology

Design and Research on Data Warehouse of Insurance Industry

2014 ◽  
Vol 496-500 ◽  
pp. 2108-2111
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Xin You Cui ◽  
Yong Wu ◽  
Lin Tao Li
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Domain Knowledge ◽  
Insurance Industry ◽  
Basic Flow ◽  
Logical Model ◽  
Mining Technology ◽  
Concept Model ◽  
Design And Implementation

Through in-depth understanding of the domain knowledge of insurance and the study of the technology of data warehouse, the paper illustrate the application of data mining technology and data warehouse technology in the insurance clients analysis, and from the basic flow of, discusse the application of data warehouse technology in the field of insurance industry. Then, from the concept of data warehouse, describe the design and implementation of data warehouse concept model and logical model.

A Holistic View of Big Data

Big Data ◽  
2016 ◽  
pp. 73-84 ◽  
Author(s):  
Won Kim ◽  
Ok-Ran Jeong ◽  
Chulyun Kim
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Open Source ◽  
Data Warehouse ◽  
Social Web ◽  
Web Data ◽  
Mining Technology ◽  
Holistic View

Today there is much hype about big data. The discussions seem to revolve around data mining technology, social Web data, and the open source platform of NoSQL and Hadoop. However, database, data warehouse and OLAP technologies are also integral parts of big data. Big data involves data from all sources, not just social Web data. Further, big data requires not only technology, but also a painstaking process for identifying, collecting, and preparing sufficient amounts of relevant data. This paper provides a holistic view of big data.

A Holistic View of Big Data

2014 ◽  
Vol 10 (3) ◽  
pp. 59-69 ◽  
Author(s):  
Won Kim ◽  
Ok-Ran Jeong ◽  
Chulyun Kim
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Open Source ◽  
Data Warehouse ◽  
Social Web ◽  
Web Data ◽  
Mining Technology ◽  
Holistic View

Today there is much hype about big data. The discussions seem to revolve around data mining technology, social Web data, and the open source platform of NoSQL and Hadoop. However, database, data warehouse and OLAP technologies are also integral parts of big data. Big data involves data from all sources, not just social Web data. Further, big data requires not only technology, but also a painstaking process for identifying, collecting, and preparing sufficient amounts of relevant data. This paper provides a holistic view of big data.

scholarly journals Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 55 ◽  
Author(s):  
Xuejun Zhu ◽  
Xiaona Jin ◽  
Dongdong Jia ◽  
Naiwei Sun ◽  
Pu Wang
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Rock Burst ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Monitoring Data ◽  
Efficient Production ◽  
Mining Technology ◽  
Rock Bursts

In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real-time queries and the analysis of monitoring data through browsers, which is very convenient. Hence, it can make important contributions to the safe and efficient production of coal mines and the sustainable development of the coal economy.

Data Mining and its Application in Modern Animal Husbandry

2014 ◽  
Vol 926-930 ◽  
pp. 2533-2536
Author(s):  
Zhi Ming Zhang ◽  
Ya Juan Sun ◽  
Tai Yu Liu ◽  
Lei Xu
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Warehouse ◽  
Animal Husbandry ◽  
Management Decisions ◽  
Support Functions ◽  
Mining Technology ◽  
Data Mining Techniques

Modern animal husbandry information technology, networking and intelligent development of data mining techniques proposed requirements. This paper analyzes the data mining for the importance of modern animal husbandry management, data warehouse, data mining technology, focusing on the analysis of data mining in the mining process in the modern animal husbandry management decisions and implementation of technology, pointed out the data mining technology provide more powerful support functions for modern animal husbandry management decisions.

scholarly journals Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

2022 ◽  
Vol 34 (3) ◽  
pp. 1-18
Author(s):  
Yanan Song ◽  
Xiaolong Hua
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Warehouse ◽  
Business Process ◽  
Processing System ◽  
Order Number ◽  
Processing Trade ◽  
Mining Technology ◽  
Decision Making System ◽  
Analytical Processing

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

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